internships
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internships [2025/08/09 13:22] – [General Requirements] respai-vic | internships [2025/09/10 16:42] (current) – [Internship proposals 2025-2026] respai-vic | ||
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==== General Requirements ==== | ==== General Requirements ==== | ||
- | For first and second year, internship topics are to be validated by the academic co-directors of the MScT (Marie-Paule Cani, Johannes Lutzeyer, Erwan Le Pennec | + | For first and second year, internship topics are to be validated by the academic co-directors of the MScT (Johannes Lutzeyer |
**First-year Internship** | **First-year Internship** | ||
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Period: from the beginning of April to the end of August (at least 4 months) | Period: from the beginning of April to the end of August (at least 4 months) | ||
- | Defence: First half of September | + | Defense: First half of September |
An internship either in a private company or in a public lab. The research component of the internship is not mandatory but strongly encouraged. Students are requested to choose a topic related to the curriculum of their graduate degree program, i.e., involving either machine learning or visual computing, ideally both (see the first and second-year courses for details). This work can be theoretical (comprehension of a theorem and extension to a new setting), applied (developing and implementing a new solution), or a mix of theory and application. | An internship either in a private company or in a public lab. The research component of the internship is not mandatory but strongly encouraged. Students are requested to choose a topic related to the curriculum of their graduate degree program, i.e., involving either machine learning or visual computing, ideally both (see the first and second-year courses for details). This work can be theoretical (comprehension of a theorem and extension to a new setting), applied (developing and implementing a new solution), or a mix of theory and application. | ||
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Period: from the beginning of April to the end of September (at least 5 months) | Period: from the beginning of April to the end of September (at least 5 months) | ||
- | Defence: First half of September (the internship can be continued after the defence) | + | Defense: First half of September (the internship can be continued after the defence) |
A research internship either in the R&D department of a company or in a public research lab. The student must produce original work related to machine learning and/or to visual computing, ideally involving several different topics studied during the whole curriculum. This work can be theoretical or applied as soon as it contains novel ideas developed and validated by the student. | A research internship either in the R&D department of a company or in a public research lab. The student must produce original work related to machine learning and/or to visual computing, ideally involving several different topics studied during the whole curriculum. This work can be theoretical or applied as soon as it contains novel ideas developed and validated by the student. | ||
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- | **Tips on how to find internships, | + | ** 💡💡 |
- | Thank you all for contributing to this feedback loop | + | Thank you all for contributing to this useful |
* **Start very very early**, beginning of October preferably | * **Start very very early**, beginning of October preferably | ||
- | * I handed out my CV at the fair and spoke for some time to the recruiter, and they contacted me later. | + | * I handed out my CV at the fair and spoke for some time to the recruiter, and they contacted me later. Tips: Get as many details as you can about the company, projects and interview process at the fair, take notes, prepare accordingly and bring up the details you found interesting during the interviews. |
- | | + | |
* Talk to as many people as you can (reduce uncertainty and put yourself on the map) | * Talk to as many people as you can (reduce uncertainty and put yourself on the map) | ||
* To strengthen internship pipelines, we should tap into our alumni network and boost visibility between former interns and incoming students. | * To strengthen internship pipelines, we should tap into our alumni network and boost visibility between former interns and incoming students. | ||
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* X-forum is a good chance -- If you try more, you have more -- Networking is all you need. | * X-forum is a good chance -- If you try more, you have more -- Networking is all you need. | ||
- | =============== Internship proposals 2024-2025 =============================================== | ||
**<color #e32d2d> For M1 students: Access internship proposals from the Polytechnique engineering student page. </ | **<color #e32d2d> For M1 students: Access internship proposals from the Polytechnique engineering student page. </ | ||
- | --- Forwarded from Damien Rohmer: ---- | ||
If you would like to see more internship proposals in Visual Computing, preselected for their decent research components, please follow this procedure: | If you would like to see more internship proposals in Visual Computing, preselected for their decent research components, please follow this procedure: | ||
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* login: your email address | * login: your email address | ||
* password: 3AstudentDIX | * password: 3AstudentDIX | ||
+ | |||
+ | =============== Internship proposals 2025-2026 =============================================== | ||
+ | |||
+ | **<color #e32d2d> Visual Recognition Group (CTU), VISTA (École Polytechnique), | ||
+ | * Locations: Paris (France) or Prague (Czech Republic) | ||
+ | * Duration: 4–6 months | ||
+ | * Requirements: | ||
+ | |||
+ | * A Generative Approach to Global Visual Geolocation: | ||
+ | * Attacking Google Image Search [[https:// | ||
+ | * Interpretable Image-to-Image Similarity with Deep Models [[https:// | ||
+ | * Deep Ensembles for Image-to-Image Similarity with Local Descriptors [[https:// | ||
+ | * Training Visual Search Models for Conflicting Logo Identification via Personalized Generative Data [[https:// | ||
+ | * Open-Vocabulary Zero-Shot Co-Segmentation with Vision-Language Models [[https:// | ||
+ | * Exploring Memorization in Generative Models via Large-Scale Image Retrieval [[https:// | ||
+ | | ||
+ | =============== Internship proposals 2024-2025 =============================================== | ||
**<color #e32d2d> CALYPSO project, Lille University (M2) </ | **<color #e32d2d> CALYPSO project, Lille University (M2) </ |
internships.1754745776.txt.gz · Last modified: 2025/08/09 13:22 by respai-vic